Lesson 2: Teaching Machines - The AI Training Game
Evolve AI Institute LLC
Name:
Date: Class:
| Criteria | Exemplary (4) | Proficient (3) | Developing (2) | Beginning (1) |
|---|---|---|---|---|
| Understanding of Machine Learning | Clearly and accurately explains machine learning concept using specific, detailed examples from the activity. Shows deep understanding. | Accurately explains machine learning concept with appropriate example from activity. Shows good understanding. | Partially explains machine learning concept. Example may be vague or incomplete. Shows some understanding. | Explanation is unclear, incorrect, or missing. Shows minimal understanding of machine learning. |
| Role of Training Data | Thoroughly explains why AI needs training data with clear comparisons between few vs. many examples. Provides insightful analysis. | Explains importance of training data and describes difference between few and many examples adequately. | Partially explains training data importance. Comparison between few/many examples is incomplete. | Explanation is minimal, unclear, or missing. Does not demonstrate understanding. |
| Data Visualization (Graph) | Graph is complete, accurate, properly labeled with title and axis labels. Data clearly shows improvement. Professional quality. | Graph is complete and mostly accurate with proper labels. Data is clear and shows improvement. | Graph is incomplete or has errors. Some labels missing. Data is unclear or partially incorrect. | Graph is missing, largely incorrect, or shows minimal effort. Labels absent or wrong. |
| Real-World Applications | Identifies 3+ relevant AI applications with detailed, accurate explanations of training methods for each. | Identifies 3 relevant AI applications with reasonable explanations of training methods. | Identifies 2 AI applications with vague or partially correct training explanations. | Identifies fewer than 2 applications or shows misconceptions about AI training. |
| Personal Connection | Provides thoughtful, detailed examples of diverse training data needed (lighting, angles, expressions, etc.). Shows critical thinking. | Provides several appropriate examples of training data variations needed. | Provides limited examples with minimal variety or detail. | Examples are minimal, unclear, or show misunderstanding of training requirements. |
| Reflection Quality | Reflection is thoughtful, specific, and insightful. Clearly explains why learning was interesting/surprising with personal connection. | Reflection is clear and relevant. Explains what was learned and why it was interesting. | Reflection is brief or superficial. Limited explanation of learning or interest. | Reflection is minimal, unclear, or missing. Shows little engagement with material. |
Total Points: / 24 points
Percentage: %
Letter Grade:
| Criteria | Exemplary (4) | Proficient (3) | Developing (2) | Beginning (1) |
|---|---|---|---|---|
| Role Performance | Consistently fulfilled assigned role with excellence. Showed leadership and initiative throughout activity. | Fulfilled assigned role effectively. Participated actively in all phases of activity. | Partially fulfilled role. Participation was inconsistent or required prompting. | Minimally fulfilled role. Limited participation or frequent off-task behavior. |
| Collaboration & Communication | Communicated clearly and respectfully. Actively listened and built on others' ideas. Excellent team player. | Communicated effectively with group members. Listened and contributed appropriately. | Communication was limited or sometimes unclear. Occasional difficulty working with others. | Poor communication. Did not work well with group or disrupted group work. |
| Pattern Recognition (Trainers/AI) | Identified multiple detailed patterns across categories. Descriptions were specific and helped AI learn effectively. | Identified key patterns in most categories. Descriptions were clear and helpful. | Identified some patterns but descriptions lacked detail or consistency. | Failed to identify patterns or gave vague, unhelpful descriptions. |
| Data Recording Accuracy | All data recorded completely and accurately. Calculations correct. Organized and thorough. | Most data recorded accurately with minor errors. Calculations mostly correct. | Some data incomplete or inaccurate. Several calculation errors. | Data recording incomplete or highly inaccurate. Major errors in calculations. |
| Learning & Improvement | Showed significant improvement from Round 1 to Round 2 (20%+ increase). Applied feedback effectively. | Showed clear improvement from Round 1 to Round 2 (10-20% increase). Used feedback. | Showed minimal improvement (5-10% increase) or inconsistent learning. | Showed no improvement or negative change. Did not apply feedback. |
| Engagement & Focus | Fully engaged throughout entire activity. Showed enthusiasm and curiosity. Asked thoughtful questions. | Engaged during most of activity. Showed interest and asked relevant questions. | Engagement was inconsistent. Sometimes distracted or off-task. | Minimally engaged. Frequently distracted or disruptive. |
Total Points: / 24 points
Percentage: %
Letter Grade:
| Criteria | Exemplary (4) | Proficient (3) | Developing (2) | Beginning (1) |
|---|---|---|---|---|
| Vocabulary Use | Uses lesson vocabulary accurately and confidently (training data, pattern, accuracy, machine learning) in multiple contexts. | Uses lesson vocabulary correctly most of the time in discussion. | Uses some vocabulary but with occasional errors or hesitation. | Rarely uses lesson vocabulary or uses it incorrectly. |
| Connecting Activity to Real AI | Makes insightful connections between activity and multiple real-world AI applications. Explains similarities clearly. | Makes appropriate connections between activity and real AI systems. | Makes limited or superficial connections to real AI. | Cannot connect activity to real-world AI applications. |
| Understanding Data Impact | Clearly articulates how more data improves AI performance. Can explain with specific examples and reasoning. | Understands and can explain that more data improves AI. | Shows partial understanding of data's role in AI learning. | Does not understand or cannot explain how data affects AI. |
| Critical Thinking | Asks thoughtful questions. Considers implications of AI training (bias, errors, ethics). Shows deeper analysis. | Asks relevant questions. Shows some critical thinking about AI. | Asks basic questions. Limited critical thinking evident. | Does not ask questions or show critical thinking about concepts. |
Total Points: / 16 points
Percentage: %
Letter Grade:
Total: / 64 points
Overall Grade:
A: 57-64 points (90-100%) - Exemplary understanding and performance
B: 51-56 points (80-89%) - Proficient understanding and performance
C: 45-50 points (70-79%) - Developing understanding, meets minimum standards
D: 38-44 points (60-69%) - Limited understanding, needs improvement
F: 0-37 points (Below 60%) - Does not meet standards, significant remediation needed
Strengths:
Areas for Growth:
Next Steps: